MRAC-MU Online Learning

Siyun Zhang, Jian-wei Liu, Xin Zuo, X. Wan, M. Kamel
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Abstract

In this paper, we apply the method of control theory to machine learning, proposing a new multiplication update algorithm combined with adaptive control theory, we name it MRAC-MU algorithm. A new parameter updating law is obtained according to Lyapunov stability theorem. Using the same object function as the exponential gradient (EG) algorithm, which is the key online learning method to multiplicative updates algorithm, Experiments are used to validate the proposed algorithm has a better result than EG algorithm in prediction accuracy.
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MRAC-MU在线学习
本文将控制理论的方法应用到机器学习中,提出了一种结合自适应控制理论的乘法更新算法,我们将其命名为MRAC-MU算法。根据李雅普诺夫稳定性定理,得到了一种新的参数更新规律。利用与乘式更新算法的关键在线学习方法指数梯度(EG)算法相同的目标函数,通过实验验证了该算法在预测精度上优于EG算法。
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